Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes.

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver-derived transcriptomics datasets (ntotal = 137) which enabled ML-methods to classify spaceflown vs. ground control animals (AUC ≥ 0.87) while mitigating the variability from mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

A multi-omics longitudinal study of the murine retinal response to chronic low-dose irradiation and simulated microgravity.

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation difference was seen across different conditions at the same time point. Nucleotide metabolism biological processes were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. A total of 23 genes showed significant changes in methylation and expression compared to unexposed controls, including genes involved in retinal function and inflammatory response. This multi-omics analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination and highlights important molecular mechanisms at different post-exposure stages.

Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.

New observations on maternal age effect on germline de novo mutations.

Germline mutations are the source of evolution and contribute substantially to many health-related processes. Here we use whole-genome deep sequencing data from 693 parents-offspring trios to examine the de novo point mutations (DNMs) in the offspring. Our estimate for the mutation rate per base pair per generation is 1.05 × 10(-8), well within the range of previous studies. We show that maternal age has a small but significant correlation with the total number of DNMs in the offspring after controlling for paternal age (0.51 additional mutations per year, 95% CI: 0.29, 0.73), which was not detectable in the smaller and younger parental cohorts of earlier studies. Furthermore, while the total number of DNMs increases at a constant rate for paternal age, the contribution from the mother increases at an accelerated rate with age.These observations have implications related to the incidence of de novo mutations relating to maternal age.

A chromosomal region 7p11.2 transcript map: its development and application to the study of EGFR amplicons in glioblastoma.

Cumulative information available about the organization of amplified chromosomal regions in human tumors suggests that the amplification repeat units, or amplicons, can be of a simple or complex nature. For the former, amplified regions generally retain their native chromosomal configuration and involve a single amplification target sequence. For complex amplicons, amplified DNAs usually undergo substantial reorganization relative to the normal chromosomal regions from which they evolve, and the regions subject to amplification may contain multiple target sequences. Previous efforts to characterize the 7p11.2 epidermal growth factor receptor ) amplicon in glioblastoma have relied primarily on the use of markers positioned by linkage analysis and/or radiation hybrid mapping, both of which are known to have the potential for being inaccurate when attempting to order loci over relatively short (<1 Mb) chromosomal regions. Due to the limited resolution of genetic maps that have been established through the use of these approaches, we have constructed a 2-Mb bacterial and P1-derived artificial chromosome (BAC-PAC) contig for the EGFR region and have applied markers positioned on its associated physical map to the analysis of 7p11.2 amplifications in a series of glioblastomas. Our data indicate that EGFR is the sole amplification target within the mapped region, although there are several additional 7p11.2 genes that can be coamplified and overexpressed with EGFR. Furthermore, these results are consistent with EGFR amplicons retaining the same organization as the native chromosome 7p11.2 region from which they are derived.